Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/100403
Title: Grid-based local feature bundling for efficient object search and localization
Authors: Jiang, Yuning
Meng, Jingjing
Yuan, Junsong
Keywords: DRNTU::Engineering::Computer science and engineering::Computing methodologies::Image processing and computer vision
Issue Date: 2011
Source: Jiang, Y., Meng, J. & Yuan, J. (2011). Grid-based Local Feature Bundling for Efficient Object Search And Localization. 18th IEEE International Conference on Image Processing (ICIP 2011), 113-116.
Abstract: We propose a new grid-based image representation for dis- criminative visual object search, with the goal to efficiently locate the query object in a large image collection. After ex- tracting local invariant features, we partition the image into non-overlapped rectangular grid cells. Each grid bundles the local features within it and is characterized by a histogram of visual words. Given both positive and negative queries, each grid is assigned a mutual information score to match and lo- cate the query object. This new image representation brings in two great benefits for efficient object search: 1) as the grid bundles local features, the spatial contextual information en- hances the discriminative matching; and 2) it enables faster object localization by searching visual object on the grid-level image. To evaluate our approach, we perform experiments on a very challenging logo database BelgaLogos [1] of 10,000 images. The comparison with the state-of-the-art methods highlights the effectiveness of our approach in both accuracy and speed.
URI: https://hdl.handle.net/10356/100403
http://hdl.handle.net/10220/18118
DOI: http://dx.doi.org/10.1109/ICIP.2011.6115629
Rights: © 2011 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: http://dx.doi.org/10.1109/ICIP.2011.6115629.
metadata.item.grantfulltext: open
metadata.item.fulltext: With Fulltext
Appears in Collections:EEE Conference Papers

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